keras datagenerator with mask
seed = 909 # (IMPORTANT) to transform image and corresponding mask with same augmentation parameter.
image_datagen = ImageDataGenerator(width_shift_range=0.1,
height_shift_range=0.1,
preprocessing_function = image_preprocessing) # custom fuction for each image you can use resnet one too.
mask_datagen = ImageDataGenerator(width_shift_range=0.1,
height_shift_range=0.1,
preprocessing_function = mask_preprocessing) # to make mask as feedable formate (256,256,1)
image_generator =image_datagen.flow_from_directory("dataset/image/",
class_mode=None, seed=seed)
mask_generator = mask_datagen.flow_from_directory("dataset/mask/",
class_mode=None, seed=seed)
train_generator = zip(image_generator, mask_generator)